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Title: Data Assimilation - Advances and Applications

Abstract

This presentation provides an overview of data assimilation (model calibration) for complex computer experiments. Calibration refers to the process of probabilistically constraining uncertain physics/engineering model inputs to be consistent with observed experimental data. An initial probability distribution for these parameters is updated using the experimental information. Utilization of surrogate models and empirical adjustment for model form error in code calibration form the basis for the statistical methodology considered. The role of probabilistic code calibration in supporting code validation is discussed. Incorporation of model form uncertainty in rigorous uncertainty quantification (UQ) analyses is also addressed. Design criteria used within a batch sequential design algorithm are introduced for efficiently achieving predictive maturity and improved code calibration. Predictive maturity refers to obtaining stable predictive inference with calibrated computer codes. These approaches allow for augmentation of initial experiment designs for collecting new physical data. A standard framework for data assimilation is presented and techniques for updating the posterior distribution of the state variables based on particle filtering and the ensemble Kalman filter are introduced.

Authors:
 [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1148964
Report Number(s):
LA-UR-14-25968
DOE Contract Number:  
AC52-06NA25396
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
22 GENERAL STUDIES OF NUCLEAR REACTORS; 36 MATERIALS SCIENCE; 97 MATHEMATICS AND COMPUTING

Citation Formats

Williams, Brian J. Data Assimilation - Advances and Applications. United States: N. p., 2014. Web. doi:10.2172/1148964.
Williams, Brian J. Data Assimilation - Advances and Applications. United States. doi:10.2172/1148964.
Williams, Brian J. Wed . "Data Assimilation - Advances and Applications". United States. doi:10.2172/1148964. https://www.osti.gov/servlets/purl/1148964.
@article{osti_1148964,
title = {Data Assimilation - Advances and Applications},
author = {Williams, Brian J.},
abstractNote = {This presentation provides an overview of data assimilation (model calibration) for complex computer experiments. Calibration refers to the process of probabilistically constraining uncertain physics/engineering model inputs to be consistent with observed experimental data. An initial probability distribution for these parameters is updated using the experimental information. Utilization of surrogate models and empirical adjustment for model form error in code calibration form the basis for the statistical methodology considered. The role of probabilistic code calibration in supporting code validation is discussed. Incorporation of model form uncertainty in rigorous uncertainty quantification (UQ) analyses is also addressed. Design criteria used within a batch sequential design algorithm are introduced for efficiently achieving predictive maturity and improved code calibration. Predictive maturity refers to obtaining stable predictive inference with calibrated computer codes. These approaches allow for augmentation of initial experiment designs for collecting new physical data. A standard framework for data assimilation is presented and techniques for updating the posterior distribution of the state variables based on particle filtering and the ensemble Kalman filter are introduced.},
doi = {10.2172/1148964},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Jul 30 00:00:00 EDT 2014},
month = {Wed Jul 30 00:00:00 EDT 2014}
}

Technical Report:

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